(1) Field of the Invention
The present invention relates to performing adaptive beamforming and, more particularly, to performing adaptive beamforming using a synthetic covariance matrix.
(2) Description of the Prior Art
Beamforming or spatial filtering is a signal processing technique used for directional signal transmission or reception. Beamforming increases the gain in a particular direction, and decreases gain in other directions. Beamforming may be used in sensor arrays, for examples, and can be utilized on electromagnetic arrays such as antennas and/or acoustic arrays. These systems include active or passive radar or sonar. Arrays of sensors include linear, planar and volumetric arrays. Beamforming is also used in communications to limit reception to a particular cell phone tower. Beamforming operates with the understanding that the beam pattern resulting from beamforming has a main lobe, side lobes, and nulls. The main lobe has the greatest gain and is at the look angle. The side lobes are angular regions having lesser gain. Nulls are angular regions having gain theoretically reduced to zero. Beamforming is also range dependent. When the range is unknown, it is known to beamform for groups of ranges known as range bins. Different look angles and ranges can be obtained by using different delays on the outputs of the sensor elements.
Adaptive beamforming adapts to the data received from the sensor array by placing nulls at the angles of arrival of the largest amount of clutter. To accomplish this, adaptive beamformers estimate the data covariance matrix. The data covariance matrix is typically estimated by the sample covariance matrix (SCM). The data covariance matrix is an Ne by Ne positive definite (and therefor nonsingular) matrix, where Ne is the number of elements in the array. For the SCM to be a nonsingular matrix, it must be produced using at least the same number of snapshots, Ns, as number of elements, Ne. A snapshot is a measurement of the signal levels of all the array element outputs at a particular time. The interval between snap shots, called the sampling frequency, is chosen in accordance with sampling theory. It is acknowledged in the art that a good estimate of the SCM requires a minimum of 2*Ne snapshots. Collecting many snapshots in a non-stationary environment is a problem, since the actual covariance changes over time.
Adaptive beamforming to remove interference can require capturing multiple snapshots of the signal to determine where to reduce the interference. This can be problematic in a non-stationary environment. Development of improved techniques of beamforming is desirable for these environments.
One prior art example includes a correlation filter for target suppression, a weight calculation method, a weight calculation device, an adaptive array antenna, and a radar device. A beam forming unit is configured to form a received synthetic beam by performing weight control based on the adaptive weight on the received signal to nullify a gain in a direction other than an arrival direction of the target signal.
Another example generally discusses an apparatus and method for forming a forward link transmission beam of a smart antenna in a mobile communication system. Angle of arrival (AoA) and beamwidth are estimated from the transmission covariance matrix. For a forward link channel to a mobile station can be estimated by synthesizing a covariance matrix considering a difference between transmission and reception frequency bands from the estimated AoA and beamwidth.
However, the above attempts do not disclose, teach, or suggest the presently disclosed method of reducing the snapshots for adaptive beamforming or providing a method that can be used in a non-stationary environment. Thus, there is a need for improved adaptive beamforming.